Final Project¶

In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import os
%matplotlib inline
from matplotlib.pyplot import figure 
from imblearn.over_sampling import SMOTE
from scipy.stats import mstats
from sklearn.impute import KNNImputer
from sklearn.metrics import roc_auc_score
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler 
from sklearn.model_selection import TimeSeriesSplit 
from sklearn.tree import DecisionTreeRegressor 
from sklearn.metrics import mean_squared_error, r2_score 
from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier
from sklearn.ensemble import RandomForestRegressor 
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import RandomOverSampler
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
import warnings
warnings.filterwarnings('ignore')
In [2]:
df1 = pd.read_excel("/Users/shashankkv/Desktop/Sem -3/Joseph Artificial Intelligence/Final_dataset.xlsx")
In [3]:
df1.head()
Out[3]:
Age Attrition DailyRate Department DistanceFromHome Education EducationField EmployeeCount EmployeeNumber EnvironmentSatisfaction ... RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
0 41 Yes 1102 Sales 1 2 Life Sciences 1 1 2 ... 1 80 0 8 0 1 6 4 0 5
1 49 No 279 Research & Development 8 1 Life Sciences 1 2 3 ... 4 80 1 10 3 3 10 7 1 7
2 37 Yes 1373 Research & Development 2 2 Other 1 4 4 ... 2 80 0 7 3 3 0 0 0 0
3 33 No 1392 Research & Development 3 4 Life Sciences 1 5 4 ... 3 80 0 8 3 3 8 7 3 0
4 27 No 591 Research & Development 2 1 Medical 1 7 1 ... 4 80 1 6 3 3 2 2 2 2

5 rows × 34 columns

In [4]:
#df1 = df1.drop('StandardHours', axis=1)
In [5]:
#df1 = df1.drop('EmployeeNumber', axis=1)
In [6]:
#df1 = df1.drop('EmployeeCount', axis=1)
In [7]:
df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 34 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Age                       1470 non-null   int64 
 1   Attrition                 1470 non-null   object
 2   DailyRate                 1470 non-null   int64 
 3   Department                1470 non-null   object
 4   DistanceFromHome          1470 non-null   int64 
 5   Education                 1470 non-null   int64 
 6   EducationField            1470 non-null   object
 7   EmployeeCount             1470 non-null   int64 
 8   EmployeeNumber            1470 non-null   int64 
 9   EnvironmentSatisfaction   1470 non-null   int64 
 10  Gender                    1470 non-null   object
 11  HourlyRate                1470 non-null   int64 
 12  JobInvolvement            1470 non-null   int64 
 13  JobLevel                  1470 non-null   int64 
 14  JobRole                   1470 non-null   object
 15  JobSatisfaction           1470 non-null   int64 
 16  MaritalStatus             1470 non-null   object
 17  MonthlyIncome             1470 non-null   int64 
 18  MonthlyRate               1470 non-null   int64 
 19  NumCompaniesWorked        1470 non-null   int64 
 20  Over18                    1470 non-null   object
 21  OverTime                  1470 non-null   object
 22  PercentSalaryHike         1470 non-null   int64 
 23  PerformanceRating         1470 non-null   int64 
 24  RelationshipSatisfaction  1470 non-null   int64 
 25  StandardHours             1470 non-null   int64 
 26  StockOptionLevel          1470 non-null   int64 
 27  TotalWorkingYears         1470 non-null   int64 
 28  TrainingTimesLastYear     1470 non-null   int64 
 29  WorkLifeBalance           1470 non-null   int64 
 30  YearsAtCompany            1470 non-null   int64 
 31  YearsInCurrentRole        1470 non-null   int64 
 32  YearsSinceLastPromotion   1470 non-null   int64 
 33  YearsWithCurrManager      1470 non-null   int64 
dtypes: int64(26), object(8)
memory usage: 390.6+ KB
In [8]:
df1.isnull().sum()
Out[8]:
Age                         0
Attrition                   0
DailyRate                   0
Department                  0
DistanceFromHome            0
Education                   0
EducationField              0
EmployeeCount               0
EmployeeNumber              0
EnvironmentSatisfaction     0
Gender                      0
HourlyRate                  0
JobInvolvement              0
JobLevel                    0
JobRole                     0
JobSatisfaction             0
MaritalStatus               0
MonthlyIncome               0
MonthlyRate                 0
NumCompaniesWorked          0
Over18                      0
OverTime                    0
PercentSalaryHike           0
PerformanceRating           0
RelationshipSatisfaction    0
StandardHours               0
StockOptionLevel            0
TotalWorkingYears           0
TrainingTimesLastYear       0
WorkLifeBalance             0
YearsAtCompany              0
YearsInCurrentRole          0
YearsSinceLastPromotion     0
YearsWithCurrManager        0
dtype: int64
In [9]:
df1.describe().T
Out[9]:
count mean std min 25% 50% 75% max
Age 1470.0 36.923810 9.135373 18.0 30.00 36.0 43.00 60.0
DailyRate 1470.0 802.485714 403.509100 102.0 465.00 802.0 1157.00 1499.0
DistanceFromHome 1470.0 9.192517 8.106864 1.0 2.00 7.0 14.00 29.0
Education 1470.0 2.912925 1.024165 1.0 2.00 3.0 4.00 5.0
EmployeeCount 1470.0 1.000000 0.000000 1.0 1.00 1.0 1.00 1.0
EmployeeNumber 1470.0 1024.865306 602.024335 1.0 491.25 1020.5 1555.75 2068.0
EnvironmentSatisfaction 1470.0 2.721769 1.093082 1.0 2.00 3.0 4.00 4.0
HourlyRate 1470.0 65.891156 20.329428 30.0 48.00 66.0 83.75 100.0
JobInvolvement 1470.0 2.729932 0.711561 1.0 2.00 3.0 3.00 4.0
JobLevel 1470.0 2.063946 1.106940 1.0 1.00 2.0 3.00 5.0
JobSatisfaction 1470.0 2.728571 1.102846 1.0 2.00 3.0 4.00 4.0
MonthlyIncome 1470.0 6502.931293 4707.956783 1009.0 2911.00 4919.0 8379.00 19999.0
MonthlyRate 1470.0 14313.103401 7117.786044 2094.0 8047.00 14235.5 20461.50 26999.0
NumCompaniesWorked 1470.0 2.693197 2.498009 0.0 1.00 2.0 4.00 9.0
PercentSalaryHike 1470.0 15.209524 3.659938 11.0 12.00 14.0 18.00 25.0
PerformanceRating 1470.0 3.153741 0.360824 3.0 3.00 3.0 3.00 4.0
RelationshipSatisfaction 1470.0 2.712245 1.081209 1.0 2.00 3.0 4.00 4.0
StandardHours 1470.0 80.000000 0.000000 80.0 80.00 80.0 80.00 80.0
StockOptionLevel 1470.0 0.793878 0.852077 0.0 0.00 1.0 1.00 3.0
TotalWorkingYears 1470.0 11.279592 7.780782 0.0 6.00 10.0 15.00 40.0
TrainingTimesLastYear 1470.0 2.799320 1.289271 0.0 2.00 3.0 3.00 6.0
WorkLifeBalance 1470.0 2.761224 0.706476 1.0 2.00 3.0 3.00 4.0
YearsAtCompany 1470.0 7.008163 6.126525 0.0 3.00 5.0 9.00 40.0
YearsInCurrentRole 1470.0 4.229252 3.623137 0.0 2.00 3.0 7.00 18.0
YearsSinceLastPromotion 1470.0 2.187755 3.222430 0.0 0.00 1.0 3.00 15.0
YearsWithCurrManager 1470.0 4.123129 3.568136 0.0 2.00 3.0 7.00 17.0
In [10]:
# Create a histogram of the age distribution
plt.hist(df1['Age'])
plt.title('Age Distribution')
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.show()
In [11]:
# Create a bar plot for job role distribution using seaborn
sns.countplot(y='JobRole', data=df1)
plt.show()
In [12]:
# Create a pie chart of job satisfaction levels using plotly
fig = px.pie(df1, names='JobSatisfaction', title='Job Satisfaction levels')
fig.show()
In [13]:
# Plotting KDE for 'Age'
sns.kdeplot(data=df1, x='Age')
plt.show()
In [14]:
df1['Attrition'] = df1['Attrition'].map({'Yes': 1, 'No': 0})
In [15]:
# Identify data types of each column
data_types = df1.dtypes

# Separate categorical and numerical variables
categorical_vars = data_types[data_types == 'object'].index
numerical_vars = data_types[data_types != 'object'].index

print("Categorical Variables:", categorical_vars)
print("Numerical Variables:", numerical_vars)

Categorical Variables: Index(['Department', 'EducationField', 'Gender', 'JobRole', 'MaritalStatus',
       'Over18', 'OverTime'],
      dtype='object')
Numerical Variables: Index(['Age', 'Attrition', 'DailyRate', 'DistanceFromHome', 'Education',
       'EmployeeCount', 'EmployeeNumber', 'EnvironmentSatisfaction',
       'HourlyRate', 'JobInvolvement', 'JobLevel', 'JobSatisfaction',
       'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
       'PercentSalaryHike', 'PerformanceRating', 'RelationshipSatisfaction',
       'StandardHours', 'StockOptionLevel', 'TotalWorkingYears',
       'TrainingTimesLastYear', 'WorkLifeBalance', 'YearsAtCompany',
       'YearsInCurrentRole', 'YearsSinceLastPromotion',
       'YearsWithCurrManager'],
      dtype='object')
In [16]:
# Remove categorical columns from the dataset
df = df1.drop(categorical_vars, axis=1)
In [17]:
df
Out[17]:
Age Attrition DailyRate DistanceFromHome Education EmployeeCount EmployeeNumber EnvironmentSatisfaction HourlyRate JobInvolvement ... RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
0 41 1 1102 1 2 1 1 2 94 3 ... 1 80 0 8 0 1 6 4 0 5
1 49 0 279 8 1 1 2 3 61 2 ... 4 80 1 10 3 3 10 7 1 7
2 37 1 1373 2 2 1 4 4 92 2 ... 2 80 0 7 3 3 0 0 0 0
3 33 0 1392 3 4 1 5 4 56 3 ... 3 80 0 8 3 3 8 7 3 0
4 27 0 591 2 1 1 7 1 40 3 ... 4 80 1 6 3 3 2 2 2 2
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1465 36 0 884 23 2 1 2061 3 41 4 ... 3 80 1 17 3 3 5 2 0 3
1466 39 0 613 6 1 1 2062 4 42 2 ... 1 80 1 9 5 3 7 7 1 7
1467 27 0 155 4 3 1 2064 2 87 4 ... 2 80 1 6 0 3 6 2 0 3
1468 49 0 1023 2 3 1 2065 4 63 2 ... 4 80 0 17 3 2 9 6 0 8
1469 34 0 628 8 3 1 2068 2 82 4 ... 1 80 0 6 3 4 4 3 1 2

1470 rows × 27 columns

Checking my dataset is balanced¶

In [18]:
target_counts = df['Attrition'].value_counts()

# Calculate the balance
balance = target_counts[1] / target_counts[0]

print("Balance:", balance)

Balance: 0.1922141119221411
In [19]:
df['Attrition'].value_counts(normalize = True)*100
Out[19]:
0    83.877551
1    16.122449
Name: Attrition, dtype: float64

Checking the outliers¶

In [20]:
n_c = ['Age', 'Attrition', 'DailyRate', 'DistanceFromHome', 'Education',
       'EnvironmentSatisfaction', 'HourlyRate',
       'JobInvolvement', 'JobLevel', 'JobSatisfaction', 'MonthlyIncome',
       'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike',
       'PerformanceRating', 'RelationshipSatisfaction', 'StockOptionLevel',
       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',
       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion']

# Calculate the IQR for each numeric column
q1 = df[n_c].quantile(0.25)
q3 = df[n_c].quantile(0.75)
iqr = q3 - q1

# Define the threshold for outlier detection
threshold = 1.5

# Identify outliers
outliers = ((df[n_c] < (q1 - threshold * iqr)) | (df[n_c] > (q3 + threshold * iqr))).any(axis=1)

# Printing the indices of the outlier rows
print("Outlier indices:")
print(df[outliers].index)

Outlier indices:
Int64Index([   0,    1,    2,    4,    6,    7,    8,   10,   14,   15,
            ...
            1446, 1447, 1448, 1452, 1457, 1458, 1461, 1462, 1466, 1467],
           dtype='int64', length=827)

Visualize the outliers in dataset using box plot¶

In [21]:
plt.figure(figsize=(12, 6))
sns.boxplot(data=df[n_c])
plt.xticks(rotation=45)
plt.title("Box Plots of Numeric Columns")
plt.xlabel("Columns")
plt.ylabel("Values")
plt.show()

Handling the outliers¶

In [22]:
# Winsorize the numeric columns
df_winsorized = pd.DataFrame()
for col in n_c:
    df_winsorized[col] = mstats.winsorize(df[col], limits=[0.05, 0.05])

# Print the winsorized DataFrame
print(df_winsorized)

      Age  Attrition  DailyRate  DistanceFromHome  Education  \
0      41          1       1102                 1          2   
1      49          0        279                 8          1   
2      37          1       1373                 2          2   
3      33          0       1392                 3          4   
4      27          0        591                 2          1   
...   ...        ...        ...               ...        ...   
1465   36          0        884                23          2   
1466   39          0        613                 6          1   
1467   27          0        164                 4          3   
1468   49          0       1023                 2          3   
1469   34          0        628                 8          3   

      EnvironmentSatisfaction  HourlyRate  JobInvolvement  JobLevel  \
0                           2          94               3         2   
1                           3          61               2         2   
2                           4          92               2         1   
3                           4          56               3         1   
4                           1          40               3         1   
...                       ...         ...             ...       ...   
1465                        3          41               4         2   
1466                        4          42               2         3   
1467                        2          87               4         2   
1468                        4          63               2         2   
1469                        2          82               4         2   

      JobSatisfaction  ...  PercentSalaryHike  PerformanceRating  \
0                   4  ...                 11                  3   
1                   2  ...                 22                  4   
2                   3  ...                 15                  3   
3                   3  ...                 11                  3   
4                   2  ...                 12                  3   
...               ...  ...                ...                ...   
1465                4  ...                 17                  3   
1466                1  ...                 15                  3   
1467                2  ...                 20                  4   
1468                2  ...                 14                  3   
1469                3  ...                 12                  3   

      RelationshipSatisfaction  StockOptionLevel  TotalWorkingYears  \
0                            1                 0                  8   
1                            4                 1                 10   
2                            2                 0                  7   
3                            3                 0                  8   
4                            4                 1                  6   
...                        ...               ...                ...   
1465                         3                 1                 17   
1466                         1                 1                  9   
1467                         2                 1                  6   
1468                         4                 0                 17   
1469                         1                 0                  6   

      TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  \
0                         1                1               6   
1                         3                3              10   
2                         3                3               1   
3                         3                3               8   
4                         3                3               2   
...                     ...              ...             ...   
1465                      3                3               5   
1466                      5                3               7   
1467                      1                3               6   
1468                      3                2               9   
1469                      3                4               4   

      YearsInCurrentRole  YearsSinceLastPromotion  
0                      4                        0  
1                      7                        1  
2                      0                        0  
3                      7                        3  
4                      2                        2  
...                  ...                      ...  
1465                   2                        0  
1466                   7                        1  
1467                   2                        0  
1468                   6                        0  
1469                   3                        1  

[1470 rows x 23 columns]
In [23]:
df_winsorized.head()
Out[23]:
Age Attrition DailyRate DistanceFromHome Education EnvironmentSatisfaction HourlyRate JobInvolvement JobLevel JobSatisfaction ... PercentSalaryHike PerformanceRating RelationshipSatisfaction StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion
0 41 1 1102 1 2 2 94 3 2 4 ... 11 3 1 0 8 1 1 6 4 0
1 49 0 279 8 1 3 61 2 2 2 ... 22 4 4 1 10 3 3 10 7 1
2 37 1 1373 2 2 4 92 2 1 3 ... 15 3 2 0 7 3 3 1 0 0
3 33 0 1392 3 4 4 56 3 1 3 ... 11 3 3 0 8 3 3 8 7 3
4 27 0 591 2 1 1 40 3 1 2 ... 12 3 4 1 6 3 3 2 2 2

5 rows × 23 columns

In [24]:
df_winsorized.isnull().sum()
Out[24]:
Age                         0
Attrition                   0
DailyRate                   0
DistanceFromHome            0
Education                   0
EnvironmentSatisfaction     0
HourlyRate                  0
JobInvolvement              0
JobLevel                    0
JobSatisfaction             0
MonthlyIncome               0
MonthlyRate                 0
NumCompaniesWorked          0
PercentSalaryHike           0
PerformanceRating           0
RelationshipSatisfaction    0
StockOptionLevel            0
TotalWorkingYears           0
TrainingTimesLastYear       0
WorkLifeBalance             0
YearsAtCompany              0
YearsInCurrentRole          0
YearsSinceLastPromotion     0
dtype: int64

Correlation of the Data¶

In [25]:
correlation_matrix = df_winsorized.corr()
print(correlation_matrix)

                               Age  Attrition  DailyRate  DistanceFromHome  \
Age                       1.000000  -0.152663   0.007397         -0.005364   
Attrition                -0.152663   1.000000  -0.056929          0.080134   
DailyRate                 0.007397  -0.056929   1.000000         -0.005119   
DistanceFromHome         -0.005364   0.080134  -0.005119          1.000000   
Education                 0.204215  -0.027852  -0.016675          0.016886   
EnvironmentSatisfaction   0.011997  -0.103369   0.017921         -0.014987   
HourlyRate                0.026907  -0.008387   0.024743          0.030818   
JobInvolvement            0.032094  -0.130016   0.046121          0.011380   
JobLevel                  0.514133  -0.176362   0.001585          0.020980   
JobSatisfaction          -0.004015  -0.103481   0.030066         -0.003278   
MonthlyIncome             0.501686  -0.160078   0.007870         -0.015138   
MonthlyRate               0.033064   0.014206  -0.033727          0.029724   
NumCompaniesWorked        0.303254   0.042234   0.037608         -0.028965   
PercentSalaryHike         0.003674  -0.014745   0.024835          0.037484   
PerformanceRating         0.000116   0.002889  -0.000441          0.026738   
RelationshipSatisfaction  0.051785  -0.045872   0.007203          0.007852   
StockOptionLevel          0.028440  -0.137145   0.041204          0.042582   
TotalWorkingYears         0.679590  -0.180678   0.022683          0.001943   
TrainingTimesLastYear    -0.011059  -0.050672  -0.006538         -0.044132   
WorkLifeBalance          -0.014132  -0.063939  -0.037184         -0.028032   
YearsAtCompany            0.282408  -0.154814  -0.023541          0.012075   
YearsInCurrentRole        0.198013  -0.167308   0.009226          0.023741   
YearsSinceLastPromotion   0.196157  -0.036628  -0.036826          0.005730   

                          Education  EnvironmentSatisfaction  HourlyRate  \
Age                        0.204215                 0.011997    0.026907   
Attrition                 -0.027852                -0.103369   -0.008387   
DailyRate                 -0.016675                 0.017921    0.024743   
DistanceFromHome           0.016886                -0.014987    0.030818   
Education                  1.000000                -0.026258    0.013901   
EnvironmentSatisfaction   -0.026258                 1.000000   -0.049018   
HourlyRate                 0.013901                -0.049018    1.000000   
JobInvolvement             0.039844                -0.008278    0.043433   
JobLevel                   0.099765                -0.000028   -0.029328   
JobSatisfaction           -0.010019                -0.006784   -0.072018   
MonthlyIncome              0.087826                -0.007065   -0.015663   
MonthlyRate               -0.027015                 0.036286   -0.015349   
NumCompaniesWorked         0.129660                 0.012396    0.024992   
PercentSalaryHike         -0.012706                -0.030995   -0.008766   
PerformanceRating         -0.029011                -0.029548   -0.001716   
RelationshipSatisfaction  -0.006898                 0.007665    0.001567   
StockOptionLevel           0.018683                 0.003432    0.050529   
TotalWorkingYears          0.141106                -0.009936   -0.000307   
TrainingTimesLastYear     -0.027299                -0.018985   -0.001249   
WorkLifeBalance            0.007909                 0.027627   -0.004828   
YearsAtCompany             0.058388                 0.001144   -0.023638   
YearsInCurrentRole         0.060030                 0.027555   -0.028415   
YearsSinceLastPromotion    0.052051                 0.018618   -0.035135   

                          JobInvolvement  JobLevel  JobSatisfaction  ...  \
Age                             0.032094  0.514133        -0.004015  ...   
Attrition                      -0.130016 -0.176362        -0.103481  ...   
DailyRate                       0.046121  0.001585         0.030066  ...   
DistanceFromHome                0.011380  0.020980        -0.003278  ...   
Education                       0.039844  0.099765        -0.010019  ...   
EnvironmentSatisfaction        -0.008278 -0.000028        -0.006784  ...   
HourlyRate                      0.043433 -0.029328        -0.072018  ...   
JobInvolvement                  1.000000 -0.009833        -0.021476  ...   
JobLevel                       -0.009833  1.000000        -0.002609  ...   
JobSatisfaction                -0.021476 -0.002609         1.000000  ...   
MonthlyIncome                  -0.014740  0.934033        -0.006963  ...   
MonthlyRate                    -0.017216  0.046051         0.000932  ...   
NumCompaniesWorked              0.013932  0.147778        -0.057410  ...   
PercentSalaryHike              -0.017444 -0.031289         0.018657  ...   
PerformanceRating              -0.029071 -0.016732         0.002297  ...   
RelationshipSatisfaction        0.034297  0.019065        -0.012454  ...   
StockOptionLevel                0.021523  0.018551         0.010690  ...   
TotalWorkingYears              -0.005762  0.783599        -0.019811  ...   
TrainingTimesLastYear          -0.005673 -0.027308        -0.004992  ...   
WorkLifeBalance                -0.014617  0.036691        -0.019459  ...   
YearsAtCompany                 -0.010827  0.524932        -0.000600  ...   
YearsInCurrentRole              0.008501  0.395553         0.001805  ...   
YearsSinceLastPromotion        -0.016993  0.335968        -0.011866  ...   

                          PercentSalaryHike  PerformanceRating  \
Age                                0.003674           0.000116   
Attrition                         -0.014745           0.002889   
DailyRate                          0.024835          -0.000441   
DistanceFromHome                   0.037484           0.026738   
Education                         -0.012706          -0.029011   
EnvironmentSatisfaction           -0.030995          -0.029548   
HourlyRate                        -0.008766          -0.001716   
JobInvolvement                    -0.017444          -0.029071   
JobLevel                          -0.031289          -0.016732   
JobSatisfaction                    0.018657           0.002297   
MonthlyIncome                     -0.029931          -0.016073   
MonthlyRate                       -0.004338          -0.011236   
NumCompaniesWorked                -0.003577          -0.012245   
PercentSalaryHike                  1.000000           0.758391   
PerformanceRating                  0.758391           1.000000   
RelationshipSatisfaction          -0.039903          -0.031351   
StockOptionLevel                   0.008727           0.003506   
TotalWorkingYears                 -0.027570           0.004392   
TrainingTimesLastYear             -0.007125          -0.013402   
WorkLifeBalance                   -0.005013           0.002572   
YearsAtCompany                    -0.040809           0.009783   
YearsInCurrentRole                -0.007573           0.033691   
YearsSinceLastPromotion           -0.040823           0.013595   

                          RelationshipSatisfaction  StockOptionLevel  \
Age                                       0.051785          0.028440   
Attrition                                -0.045872         -0.137145   
DailyRate                                 0.007203          0.041204   
DistanceFromHome                          0.007852          0.042582   
Education                                -0.006898          0.018683   
EnvironmentSatisfaction                   0.007665          0.003432   
HourlyRate                                0.001567          0.050529   
JobInvolvement                            0.034297          0.021523   
JobLevel                                  0.019065          0.018551   
JobSatisfaction                          -0.012454          0.010690   
MonthlyIncome                             0.026476          0.004912   
MonthlyRate                              -0.002819         -0.035770   
NumCompaniesWorked                        0.052751          0.028898   
PercentSalaryHike                        -0.039903          0.008727   
PerformanceRating                        -0.031351          0.003506   
RelationshipSatisfaction                  1.000000         -0.045952   
StockOptionLevel                         -0.045952          1.000000   
TotalWorkingYears                         0.022837          0.014782   
TrainingTimesLastYear                     0.006717          0.009222   
WorkLifeBalance                           0.019604          0.004129   
YearsAtCompany                            0.011535          0.021939   
YearsInCurrentRole                       -0.020054          0.056152   
YearsSinceLastPromotion                   0.020279          0.016492   

                          TotalWorkingYears  TrainingTimesLastYear  \
Age                                0.679590              -0.011059   
Attrition                         -0.180678              -0.050672   
DailyRate                          0.022683              -0.006538   
DistanceFromHome                   0.001943              -0.044132   
Education                          0.141106              -0.027299   
EnvironmentSatisfaction           -0.009936              -0.018985   
HourlyRate                        -0.000307              -0.001249   
JobInvolvement                    -0.005762              -0.005673   
JobLevel                           0.783599              -0.027308   
JobSatisfaction                   -0.019811              -0.004992   
MonthlyIncome                      0.774523              -0.024360   
MonthlyRate                        0.021058              -0.003453   
NumCompaniesWorked                 0.251909              -0.065646   
PercentSalaryHike                 -0.027570              -0.007125   
PerformanceRating                  0.004392              -0.013402   
RelationshipSatisfaction           0.022837               0.006717   
StockOptionLevel                   0.014782               0.009222   
TotalWorkingYears                  1.000000              -0.032040   
TrainingTimesLastYear             -0.032040               1.000000   
WorkLifeBalance                    0.004029               0.024036   
YearsAtCompany                     0.607889               0.003478   
YearsInCurrentRole                 0.456291              -0.002654   
YearsSinceLastPromotion            0.376024               0.003595   

                          WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \
Age                             -0.014132        0.282408            0.198013   
Attrition                       -0.063939       -0.154814           -0.167308   
DailyRate                       -0.037184       -0.023541            0.009226   
DistanceFromHome                -0.028032        0.012075            0.023741   
Education                        0.007909        0.058388            0.060030   
EnvironmentSatisfaction          0.027627        0.001144            0.027555   
HourlyRate                      -0.004828       -0.023638           -0.028415   
JobInvolvement                  -0.014617       -0.010827            0.008501   
JobLevel                         0.036691        0.524932            0.395553   
JobSatisfaction                 -0.019459       -0.000600            0.001805   
MonthlyIncome                    0.030743        0.494547            0.348851   
MonthlyRate                      0.007068       -0.031016           -0.009285   
NumCompaniesWorked              -0.007625       -0.120393           -0.103091   
PercentSalaryHike               -0.005013       -0.040809           -0.007573   
PerformanceRating                0.002572        0.009783            0.033691   
RelationshipSatisfaction         0.019604        0.011535           -0.020054   
StockOptionLevel                 0.004129        0.021939            0.056152   
TotalWorkingYears                0.004029        0.607889            0.456291   
TrainingTimesLastYear            0.024036        0.003478           -0.002654   
WorkLifeBalance                  1.000000        0.013949            0.042241   
YearsAtCompany                   0.013949        1.000000            0.805600   
YearsInCurrentRole               0.042241        0.805600            1.000000   
YearsSinceLastPromotion          0.008146        0.604095            0.550674   

                          YearsSinceLastPromotion  
Age                                      0.196157  
Attrition                               -0.036628  
DailyRate                               -0.036826  
DistanceFromHome                         0.005730  
Education                                0.052051  
EnvironmentSatisfaction                  0.018618  
HourlyRate                              -0.035135  
JobInvolvement                          -0.016993  
JobLevel                                 0.335968  
JobSatisfaction                         -0.011866  
MonthlyIncome                            0.317722  
MonthlyRate                             -0.006008  
NumCompaniesWorked                      -0.049714  
PercentSalaryHike                       -0.040823  
PerformanceRating                        0.013595  
RelationshipSatisfaction                 0.020279  
StockOptionLevel                         0.016492  
TotalWorkingYears                        0.376024  
TrainingTimesLastYear                    0.003595  
WorkLifeBalance                          0.008146  
YearsAtCompany                           0.604095  
YearsInCurrentRole                       0.550674  
YearsSinceLastPromotion                  1.000000  

[23 rows x 23 columns]
In [26]:
plt.figure(figsize=(25, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.show()

Splitting the Data¶

In [27]:
X = df_winsorized.drop(columns=["Attrition"])
y = df_winsorized['Attrition']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Print the shape of the training and testing sets
print("X_train shape:", X_train.shape)
print("X_test shape:", X_test.shape)
print("y_train shape:", y_train.shape)
print("y_test shape:", y_test.shape)

X_train shape: (1176, 22)
X_test shape: (294, 22)
y_train shape: (1176,)
y_test shape: (294,)

Balancing my dat¶

In [28]:
print("Before OverSampling, counts of label '1':",np.sum(y == 1))
print("Before OverSampling, counts of label '0':",np.sum(y == 0)) 
  

sm = SMOTE(random_state = 42) 
X_res, y_res = sm.fit_resample(X_train, y_train)
  
print('After OverSampling, the shape of train_X: {}'.format(X_res.shape)) 
print('After OverSampling, the shape of train_y: {} \n'.format(y_res.shape)) 
  
print("After OverSampling, counts of label '1': {}".format(sum(y_res == 1))) 
print("After OverSampling, counts of label '0': {}".format(sum(y_res == 0)))

Before OverSampling, counts of label '1': 237
Before OverSampling, counts of label '0': 1233
After OverSampling, the shape of train_X: (1956, 22)
After OverSampling, the shape of train_y: (1956,) 

After OverSampling, counts of label '1': 978
After OverSampling, counts of label '0': 978

Model Building¶

Logistic Regression¶

In [29]:
X_train1, X_test1, y_train1, y_test1 = train_test_split(X_res,y_res,test_size=0.3,random_state=15)

Train Scores:¶

In [30]:
lr1 = LogisticRegression() 
lr1.fit(X_train1, y_train1) 
trainpred2 = lr1.predict(X_train1) 
  
 
print(classification_report(y_train1, trainpred2)) 

              precision    recall  f1-score   support

           0       0.71      0.65      0.68       691
           1       0.67      0.73      0.70       678

    accuracy                           0.69      1369
   macro avg       0.69      0.69      0.69      1369
weighted avg       0.69      0.69      0.69      1369
In [31]:
conf_mat = confusion_matrix(y_train1, trainpred2)
sns.heatmap(conf_mat, annot=True, fmt='d', cbar=False)
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Actual vs. Predicted Confusion Matrix')
plt.show()
In [32]:
a = confusion_matrix(y_train1, trainpred2)
TN = a[0,0]
FP = a[0,1]
FN = a[1,0]
TP = a[1,1]
precision = TP/float(TP+FP)
Recall = TP/float(TP+FN)
In [33]:
print("Precision:", precision)
print("Recall:",Recall)
print("roc_auc_Score:",roc_auc_score(y_train1, trainpred2) )
print("f1_score:",f1_score(y_train1, trainpred2) )
print("accuracy_score:",accuracy_score(y_train1, trainpred2))
x = (precision, Recall, roc_auc_score(y_train1, trainpred2), f1_score(y_train1, trainpred2) )

Precision: 0.6734972677595629
Recall: 0.7271386430678466
roc_auc_Score: 0.6906315501880478
f1_score: 0.699290780141844
accuracy_score: 0.6902848794740687

TestScores:¶

In [34]:
predictions1 = lr1.predict(X_test1) 
print(classification_report(y_test1, predictions1)) 

              precision    recall  f1-score   support

           0       0.73      0.66      0.69       287
           1       0.70      0.77      0.73       300

    accuracy                           0.72       587
   macro avg       0.72      0.71      0.71       587
weighted avg       0.72      0.72      0.71       587
In [35]:
conf_mat = confusion_matrix(y_test1, predictions1)
sns.heatmap(conf_mat, annot=True, fmt='d', cbar=False)
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Actual vs. Predicted Confusion Matrix')
b, t = plt.ylim() 
b += 0.5 
t -= 0.5 
plt.ylim(b, t) 
plt.show()
In [36]:
a = confusion_matrix(y_test1, predictions1)
TN = a[0,0]
FP = a[0,1]
FN = a[1,0]
TP = a[1,1]
precision = TP/float(TP+FP)
Recall = TP/float(TP+FN)

print("Precision:", precision)
print("Recall:",Recall)
print("roc_auc_Score:",roc_auc_score(y_test1, predictions1) )
print("f1_score:",f1_score(y_test1, predictions1) )
print("accuracy_score:",accuracy_score(y_test1, predictions1))

Precision: 0.7021276595744681
Recall: 0.77
roc_auc_Score: 0.7142682926829268
f1_score: 0.7344992050874404
accuracy_score: 0.7155025553662692

Decision Tree¶

Train Scores¶

In [37]:
giniDecisionTree2 = DecisionTreeClassifier(criterion='gini')

giniDecisionTree2.fit(X_train1, y_train1)
Out[37]:
DecisionTreeClassifier()
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DecisionTreeClassifier()
In [38]:
trainpred3 = giniDecisionTree2.predict(X_train1)
print(classification_report(y_train1, trainpred3))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00       691
           1       1.00      1.00      1.00       678

    accuracy                           1.00      1369
   macro avg       1.00      1.00      1.00      1369
weighted avg       1.00      1.00      1.00      1369
In [39]:
conf_mat = confusion_matrix(y_train1, trainpred3)
sns.heatmap(conf_mat, annot=True, fmt='d', cbar=False)
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Actual vs. Predicted Confusion Matrix')
b, t = plt.ylim() 
b += 0.5 
t -= 0.5 
plt.ylim(b, t) 
plt.show()
In [40]:
a = confusion_matrix(y_train1, trainpred3)
TN = a[0,0]
FP = a[0,1]
FN = a[1,0]
TP = a[1,1]
precision = TP/float(TP+FP)
Recall = TP/float(TP+FN)

print("Precision:", precision)
print("Recall:",Recall)
print("roc_auc_Score:",roc_auc_score(y_train1, trainpred3) )
print("f1_score:",f1_score(y_train1, trainpred3) )
print("accuracy_score:",accuracy_score(y_train1, trainpred3))

Precision: 1.0
Recall: 1.0
roc_auc_Score: 1.0
f1_score: 1.0
accuracy_score: 1.0

Test Scores¶

In [41]:
giniPred2 = giniDecisionTree2.predict(X_test1)
print(classification_report(y_test1, giniPred2))

              precision    recall  f1-score   support

           0       0.84      0.74      0.78       287
           1       0.77      0.86      0.82       300

    accuracy                           0.80       587
   macro avg       0.81      0.80      0.80       587
weighted avg       0.80      0.80      0.80       587
In [42]:
conf_mat = confusion_matrix(y_test1, giniPred2)
sns.heatmap(conf_mat, annot=True, fmt='d', cbar=False)
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Actual vs. Predicted Confusion Matrix')
b, t = plt.ylim() 
b += 0.5 
t -= 0.5 
plt.ylim(b, t) 
plt.show()
In [43]:
a = confusion_matrix(y_test1, giniPred2)
TN = a[0,0]
FP = a[0,1]
FN = a[1,0]
TP = a[1,1]
precision = TP/float(TP+FP)
Recall = TP/float(TP+FN)

print("Precision:", precision)
print("Recall:",Recall)
print("roc_auc_Score:",roc_auc_score(y_test1, giniPred2) )
print("f1_score:",f1_score(y_test1, giniPred2) )
print("accuracy_score:",accuracy_score(y_test1, giniPred2))

Precision: 0.7731343283582089
Recall: 0.8633333333333333
roc_auc_Score: 0.7992624854819975
f1_score: 0.8157480314960631
accuracy_score: 0.8006814310051107

Scaling the Data Set¶

In [44]:
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X=pd.DataFrame(sc.fit_transform(X),columns=X.columns)
In [45]:
print("Before OverSampling, counts of label '1':",np.sum(y == 1))
print("Before OverSampling, counts of label '0':",np.sum(y == 0)) 
  
# import SMOTE module from imblearn library 
# pip install imblearn (if you don't have imblearn in your system) 
from imblearn.over_sampling import SMOTE 
smt = SMOTE(random_state = 2) 
X_res, y_res = smt.fit_resample(X, y) 
  
print('After OverSampling, the shape of train_X: {}'.format(X_res.shape)) 
print('After OverSampling, the shape of train_y: {} \n'.format(y_res.shape)) 
  
print("After OverSampling, counts of label '1': {}".format(sum(y_res == 1))) 
print("After OverSampling, counts of label '0': {}".format(sum(y_res == 0)))

Before OverSampling, counts of label '1': 237
Before OverSampling, counts of label '0': 1233
After OverSampling, the shape of train_X: (2466, 22)
After OverSampling, the shape of train_y: (2466,) 

After OverSampling, counts of label '1': 1233
After OverSampling, counts of label '0': 1233
In [46]:
X_train1, X_test1, y_train1, y_test1 = train_test_split(X_res,y_res,test_size=0.3,random_state=15)

Logistic Regression¶

Train Scores¶

In [47]:
lr1 = LogisticRegression(max_iter = 100000) 
lr1.fit(X_train1, y_train1) 
trainpred2 = lr1.predict(X_train1) 
  
# print classification report 
print(classification_report(y_train1, trainpred2)) 

              precision    recall  f1-score   support

           0       0.74      0.71      0.72       863
           1       0.72      0.75      0.73       863

    accuracy                           0.73      1726
   macro avg       0.73      0.73      0.73      1726
weighted avg       0.73      0.73      0.73      1726
In [48]:
conf_mat = confusion_matrix(y_train1, trainpred2)
sns.heatmap(conf_mat, annot=True, fmt='d', cbar=False)
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Actual vs. Predicted Confusion Matrix')
b, t = plt.ylim() 
b += 0.5 
t -= 0.5 
plt.ylim(b, t) 
plt.show()
In [49]:
a = confusion_matrix(y_train1, trainpred2)
TN = a[0,0]
FP = a[0,1]
FN = a[1,0]
TP = a[1,1]
precision = TP/float(TP+FP)
Recall = TP/float(TP+FN)

print("Precision:", precision)
print("Recall:",Recall)
print("roc_auc_Score:",roc_auc_score(y_train1, trainpred2) )
print("f1_score:",f1_score(y_train1, trainpred2) )
print("accuracy_score:",accuracy_score(y_train1, trainpred2))

Precision: 0.7187153931339978
Recall: 0.7520278099652375
roc_auc_Score: 0.7288528389339514
f1_score: 0.7349943374858436
accuracy_score: 0.7288528389339514

Test Scores¶

In [50]:
predictions1 = lr1.predict(X_test1) 
  
# print classification report 
print(classification_report(y_test1, predictions1))

              precision    recall  f1-score   support

           0       0.73      0.70      0.71       370
           1       0.71      0.74      0.72       370

    accuracy                           0.72       740
   macro avg       0.72      0.72      0.72       740
weighted avg       0.72      0.72      0.72       740
In [51]:
conf_mat = confusion_matrix(y_test1, predictions1)
sns.heatmap(conf_mat, annot=True, fmt='d', cbar=False)
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Actual vs. Predicted Confusion Matrix')
b, t = plt.ylim() 
b += 0.5 
t -= 0.5 
plt.ylim(b, t) 
plt.show()
In [52]:
a = confusion_matrix(y_test1, predictions1)
TN = a[0,0]
FP = a[0,1]
FN = a[1,0]
TP = a[1,1]
precision = TP/float(TP+FP)
Recall = TP/float(TP+FN)

print("Precision:", precision)
print("Recall:",Recall)
print("roc_auc_Score:",roc_auc_score(y_test1, predictions1) )
print("f1_score:",f1_score(y_test1, predictions1) )
print("accuracy_score:",accuracy_score(y_test1, predictions1))

Precision: 0.7098445595854922
Recall: 0.7405405405405405
roc_auc_Score: 0.7189189189189189
f1_score: 0.7248677248677249
accuracy_score: 0.7189189189189189

Cross Validation¶

In [53]:
from sklearn import model_selection
kf=model_selection.KFold(n_splits=5,shuffle=True,random_state=0)
values=model_selection.cross_val_score(lr1,X_res, y_res,cv=kf,scoring='roc_auc')
print(values)
b_e=1-np.mean(values)
v_e=np.std(values,ddof=1)
print('Bias_error:',b_e)
print('VE:',v_e)
print("Accuracy: %0.2f (+/- %0.2f)" % (values.mean(), values.std() * 2))

[0.79036426 0.81985282 0.77215127 0.79231681 0.77022926]
Bias_error: 0.21101711569795278
VE: 0.02000611023202749
Accuracy: 0.79 (+/- 0.04)

Decision Tree¶

Train Scores¶

In [54]:
giniDecisionTree2 = DecisionTreeClassifier(criterion='gini')
giniDecisionTree2.fit(X_train1, y_train1)
Out[54]:
DecisionTreeClassifier()
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DecisionTreeClassifier()
In [55]:
trainpred3 = giniDecisionTree2.predict(X_train1)
print(classification_report(y_train1, trainpred3))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00       863
           1       1.00      1.00      1.00       863

    accuracy                           1.00      1726
   macro avg       1.00      1.00      1.00      1726
weighted avg       1.00      1.00      1.00      1726
In [56]:
conf_mat = confusion_matrix(y_train1, trainpred3)
sns.heatmap(conf_mat, annot=True, fmt='d', cbar=False)
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Actual vs. Predicted Confusion Matrix')
b, t = plt.ylim() 
b += 0.5 
t -= 0.5 
plt.ylim(b, t) 
plt.show()
In [57]:
a = confusion_matrix(y_train1, trainpred3)
TN = a[0,0]
FP = a[0,1]
FN = a[1,0]
TP = a[1,1]
precision = TP/float(TP+FP)
Recall = TP/float(TP+FN)

print("Precision:", precision)
print("Recall:",Recall)
print("roc_auc_Score:",roc_auc_score(y_train1, trainpred3) )
print("f1_score:",f1_score(y_train1, trainpred3) )
print("accuracy_score:",accuracy_score(y_train1, trainpred3))

Precision: 1.0
Recall: 1.0
roc_auc_Score: 1.0
f1_score: 1.0
accuracy_score: 1.0

Test Scores¶

In [58]:
giniPred2 = giniDecisionTree2.predict(X_test1)
print(classification_report(y_test1, giniPred2))

              precision    recall  f1-score   support

           0       0.82      0.79      0.81       370
           1       0.80      0.83      0.81       370

    accuracy                           0.81       740
   macro avg       0.81      0.81      0.81       740
weighted avg       0.81      0.81      0.81       740
In [59]:
conf_mat = confusion_matrix(y_test1, giniPred2)
sns.heatmap(conf_mat, annot=True, fmt='d', cbar=False)
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Actual vs. Predicted Confusion Matrix')
b, t = plt.ylim() 
b += 0.5 
t -= 0.5 
plt.ylim(b, t) 
plt.show()
In [60]:
a = confusion_matrix(y_test1, giniPred2)
TN = a[0,0]
FP = a[0,1]
FN = a[1,0]
TP = a[1,1]
precision = TP/float(TP+FP)
Recall = TP/float(TP+FN)

print("Precision:", precision)
print("Recall:",Recall)
print("roc_auc_Score:",roc_auc_score(y_test1, giniPred2) )
print("f1_score:",f1_score(y_test1, giniPred2) )
print("accuracy_score:",accuracy_score(y_test1, giniPred2))

Precision: 0.7994791666666666
Recall: 0.8297297297297297
roc_auc_Score: 0.8108108108108107
f1_score: 0.8143236074270556
accuracy_score: 0.8108108108108109

Cross Validation¶

In [61]:
from sklearn import model_selection
kf=model_selection.KFold(n_splits=5,shuffle=True,random_state=0)
values=model_selection.cross_val_score(giniDecisionTree2,X_res, y_res,cv=kf,scoring='roc_auc')
print(values)
b_e=1-np.mean(values)
v_e=np.std(values,ddof=1)
print('Bias_error:',b_e)
print('VE:',v_e)
print("Accuracy: %0.2f (+/- %0.2f)" % (values.mean(), values.std() * 2))

[0.83541554 0.88666139 0.84413259 0.83586806 0.83448788]
Bias_error: 0.15268690889373038
VE: 0.022335121198477054
Accuracy: 0.85 (+/- 0.04)

Random Forest¶

In [62]:
rf = RandomForestClassifier(random_state=42)
In [63]:
rf.fit(X_train1,y_train1)
Out[63]:
RandomForestClassifier(random_state=42)
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RandomForestClassifier(random_state=42)
In [64]:
rf.score(X_train1,y_train1)
Out[64]:
1.0
In [65]:
pred_rf=rf.predict(X_test1)
In [66]:
rf.predict_proba(X_test1)
Out[66]:
array([[0.15, 0.85],
       [0.12, 0.88],
       [0.37, 0.63],
       ...,
       [0.41, 0.59],
       [0.83, 0.17],
       [0.83, 0.17]])
In [67]:
print(classification_report(y_test1,pred_rf))

              precision    recall  f1-score   support

           0       0.89      0.93      0.91       370
           1       0.92      0.89      0.91       370

    accuracy                           0.91       740
   macro avg       0.91      0.91      0.91       740
weighted avg       0.91      0.91      0.91       740
In [68]:
print(confusion_matrix(y_test1,pred_rf))

[[343  27]
 [ 41 329]]
In [69]:
roc_auc_score(y_test1,pred_rf)
Out[69]:
0.908108108108108

Train Scores¶

In [70]:
pred_train2 = rf.predict(X_train1) 
 
print(confusion_matrix(y_train1, pred_train2))

[[863   0]
 [  0 863]]
In [71]:
c = confusion_matrix(y_train1, pred_train2)
TN = c[0,0]
FP = c[0,1]
FN = c[1,0]
TP = c[1,1]
precision4 = TP/float(TP+FP)
Recall4 = TP/float(TP+FN)

print("Precision:", precision4)
print("Recall:",Recall4)
print("roc_auc_Score:",roc_auc_score(y_train1, pred_train2) )
print("f1_score:",f1_score(y_train1, pred_train2) )
print("accuracy_score:",accuracy_score(y_train1, pred_train2))

Precision: 1.0
Recall: 1.0
roc_auc_Score: 1.0
f1_score: 1.0
accuracy_score: 1.0

Test Scores¶

In [72]:
print(confusion_matrix(y_test1,pred_rf))

[[343  27]
 [ 41 329]]
In [73]:
conf_mat = confusion_matrix(y_test1, pred_rf)
sns.heatmap(conf_mat, annot=True, fmt='d', cbar=False)
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Actual vs. Predicted Confusion Matrix')
b, t = plt.ylim() 
b += 0.5 
t -= 0.5 
plt.ylim(b, t) 
plt.show()
In [74]:
c1 = confusion_matrix(y_test1,pred_rf)
TN = c1[0,0]
FP = c1[0,1]
FN = c1[1,0]
TP = c1[1,1]
precision5 = TP/float(TP+FP)
Recall5 = TP/float(TP+FN)

print("Precision:", precision5)
print("Recall:",Recall5)
print("roc_auc_Score:",roc_auc_score(y_test1,pred_rf) )
print("f1_score:",f1_score(y_test1,pred_rf) )
print("accuracy_score:",accuracy_score(y_test1,pred_rf))

Precision: 0.9241573033707865
Recall: 0.8891891891891892
roc_auc_Score: 0.908108108108108
f1_score: 0.90633608815427
accuracy_score: 0.9081081081081082

Cross Validation¶

In [75]:
from sklearn import model_selection
kf=model_selection.KFold(n_splits=5,shuffle=True,random_state=0)
values=model_selection.cross_val_score(rf,X_res, y_res,cv=kf,scoring='roc_auc')
print(values)
b_e=1-np.mean(values)
v_e=np.std(values,ddof=1)
print('Bias_error:',b_e)
print('VE:',v_e)
print("Accuracy: %0.2f (+/- %0.2f)" % (values.mean(), values.std() * 2))

[0.96675061 0.98966274 0.97331947 0.98065212 0.97243114]
Bias_error: 0.02343678350322964
VE: 0.008834587212657315
Accuracy: 0.98 (+/- 0.02)

Ada-boost¶

In [76]:
ada=AdaBoostClassifier()
In [77]:
ada.fit(X_train1,y_train1)
Out[77]:
AdaBoostClassifier()
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AdaBoostClassifier()
In [78]:
ada.score(X_train1,y_train1)
Out[78]:
0.9009269988412515
In [79]:
pred_ada=ada.predict(X_test1)
In [80]:
ada.predict_proba(X_test1)
Out[80]:
array([[0.48364311, 0.51635689],
       [0.47140314, 0.52859686],
       [0.49072158, 0.50927842],
       ...,
       [0.4995125 , 0.5004875 ],
       [0.5020442 , 0.4979558 ],
       [0.50310049, 0.49689951]])
In [81]:
print(classification_report(y_test1,pred_ada))

              precision    recall  f1-score   support

           0       0.88      0.88      0.88       370
           1       0.88      0.88      0.88       370

    accuracy                           0.88       740
   macro avg       0.88      0.88      0.88       740
weighted avg       0.88      0.88      0.88       740
In [82]:
print(confusion_matrix(y_test1,pred_ada))

[[324  46]
 [ 44 326]]
In [83]:
roc_auc_score(y_test1,pred_ada)
Out[83]:
0.8783783783783783

Train Scores¶

In [84]:
pred_train1 = ada.predict(X_train1) 
 
print(confusion_matrix(y_train1, pred_train1))

[[790  73]
 [ 98 765]]
In [85]:
d = confusion_matrix(y_train1, pred_train2)
TN = d[0,0]
FP = d[0,1]
FN = d[1,0]
TP = d[1,1]
precision6 = TP/float(TP+FP)
Recall6 = TP/float(TP+FN)
In [86]:
print("Precision:", precision6)
print("Recall:",Recall6)
print("roc_auc_Score:",roc_auc_score(y_train1, pred_train1) )
print("f1_score:",f1_score(y_train1, pred_train1) )
print("accuracy_score:",accuracy_score(y_train1, pred_train1))

Precision: 1.0
Recall: 1.0
roc_auc_Score: 0.9009269988412514
f1_score: 0.8994708994708994
accuracy_score: 0.9009269988412515

Test Scores¶

In [87]:
print(confusion_matrix(y_test1,pred_ada))

[[324  46]
 [ 44 326]]
In [88]:
d1 = confusion_matrix(y_test1,pred_ada)
TN = d1[0,0]
FP = d1[0,1]
FN = d1[1,0]
TP = d1[1,1]
precision7 = TP/float(TP+FP)
Recall7 = TP/float(TP+FN)
In [89]:
print("Precision:", precision7)
print("Recall:",Recall7)
print("roc_auc_Score:",roc_auc_score(y_test1,pred_ada) )
print("f1_score:",f1_score(y_test1,pred_ada) )
print("accuracy_score:",accuracy_score(y_test1,pred_ada))

Precision: 0.8763440860215054
Recall: 0.8810810810810811
roc_auc_Score: 0.8783783783783783
f1_score: 0.8787061994609164
accuracy_score: 0.8783783783783784

Cross Validation¶

In [90]:
from sklearn import model_selection
kf=model_selection.KFold(n_splits=5,shuffle=True,random_state=0)
values=model_selection.cross_val_score(ada,X_res, y_res,cv=kf,scoring='roc_auc')
print(values)
b_e=1-np.mean(values)
v_e=np.std(values,ddof=1)
print('Bias_error:',b_e)
print('VE:',v_e)
print("Accuracy: %0.2f (+/- %0.2f)" % (values.mean(), values.std() * 2))

[0.93047257 0.97143847 0.93449493 0.95253144 0.93496619]
Bias_error: 0.0552192798461697
VE: 0.017157760065195562
Accuracy: 0.94 (+/- 0.03)

Bagging¶

In [91]:
bgc=BaggingClassifier()
In [92]:
bgc.fit(X_train1,y_train1)
Out[92]:
BaggingClassifier()
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BaggingClassifier()
In [93]:
bgc.score(X_train1,y_train1)
Out[93]:
0.9913093858632677
In [94]:
pred_bgc=bgc.predict(X_test1)
In [95]:
bgc.predict_proba(X_test1)
Out[95]:
array([[0. , 1. ],
       [0.1, 0.9],
       [0.4, 0.6],
       ...,
       [0.2, 0.8],
       [0.8, 0.2],
       [0.8, 0.2]])
In [96]:
print(classification_report(y_test1,pred_bgc))

              precision    recall  f1-score   support

           0       0.86      0.91      0.89       370
           1       0.91      0.86      0.88       370

    accuracy                           0.89       740
   macro avg       0.89      0.89      0.89       740
weighted avg       0.89      0.89      0.89       740
In [97]:
print(confusion_matrix(y_test1,pred_bgc))

[[338  32]
 [ 53 317]]
In [98]:
roc_auc_score(y_test1,pred_bgc)
Out[98]:
0.8851351351351351

Train Scores¶

In [99]:
pred_train4 = bgc.predict(X_train1) 
 
print(confusion_matrix(y_train1, pred_train4))

[[860   3]
 [ 12 851]]
In [100]:
f = confusion_matrix(y_train1, pred_train4)
TN = f[0,0]
FP = f[0,1]
FN = f[1,0]
TP = f[1,1]
precision10 = TP/float(TP+FP)
Recall10 = TP/float(TP+FN)
In [101]:
print("Precision:", precision10)
print("Recall:",Recall10)
print("roc_auc_Score:",roc_auc_score(y_train1, pred_train4) )
print("f1_score:",f1_score(y_train1, pred_train4) )
print("accuracy_score:",accuracy_score(y_train1, pred_train4))

Precision: 0.9964871194379391
Recall: 0.9860950173812283
roc_auc_Score: 0.9913093858632677
f1_score: 0.9912638322655796
accuracy_score: 0.9913093858632677

Test Scores¶

In [102]:
print(confusion_matrix(y_test1,pred_bgc))

[[338  32]
 [ 53 317]]
In [103]:
f1 = confusion_matrix(y_test1,pred_bgc)
TN = f1[0,0]
FP = f1[0,1]
FN = f1[1,0]
TP = f1[1,1]
precision11 = TP/float(TP+FP)
Recall11 = TP/float(TP+FN)
In [104]:
print("Precision:", precision11)
print("Recall:",Recall11)
print("roc_auc_Score:",roc_auc_score(y_test1,pred_bgc) )
print("f1_score:",f1_score(y_test1,pred_bgc) )
print("accuracy_score:",accuracy_score(y_test1,pred_bgc))

Precision: 0.9083094555873925
Recall: 0.8567567567567568
roc_auc_Score: 0.8851351351351351
f1_score: 0.881780250347705
accuracy_score: 0.8851351351351351

Cross Validation¶

In [105]:
from sklearn import model_selection
kf=model_selection.KFold(n_splits=5,shuffle=True,random_state=0)
values=model_selection.cross_val_score(bgc,X_res, y_res,cv=kf,scoring='roc_auc')
print(values)
b_e=1-np.mean(values)
v_e=np.std(values,ddof=1)
print('Bias_error:',b_e)
print('VE:',v_e)
print("Accuracy: %0.2f (+/- %0.2f)" % (values.mean(), values.std() * 2))

[0.92649746 0.97068772 0.95912946 0.95456416 0.92827808]
Bias_error: 0.05216862496726815
VE: 0.019575858945259397
Accuracy: 0.95 (+/- 0.04)

XGBC¶

In [106]:
import xgboost as xgb
xgbc=xgb.XGBClassifier()
In [107]:
xgbc.fit(X_train1, y_train1)
Out[107]:
XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)
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XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)
In [108]:
xgbc.score(X_train1, y_train1)
Out[108]:
1.0
In [109]:
pred_xgbc = xgbc.predict(X_test1)  
print(classification_report(y_test1, pred_xgbc))

              precision    recall  f1-score   support

           0       0.89      0.93      0.91       370
           1       0.93      0.89      0.91       370

    accuracy                           0.91       740
   macro avg       0.91      0.91      0.91       740
weighted avg       0.91      0.91      0.91       740
In [110]:
print(confusion_matrix(y_test1, pred_xgbc))

[[344  26]
 [ 42 328]]
In [111]:
roc_auc_score(y_test1, pred_xgbc)
Out[111]:
0.9081081081081082

Train Scores¶

In [112]:
pred_train5 = xgbc.predict(X_train1) 
 
print(confusion_matrix(y_train1, pred_train5))

[[863   0]
 [  0 863]]
In [113]:
g = confusion_matrix(y_train1, pred_train5)
TN = g[0,0]
FP = g[0,1]
FN = g[1,0]
TP = g[1,1]
precision12 = TP/float(TP+FP)
Recall12 = TP/float(TP+FN)
In [114]:
print("Precision:", precision12)
print("Recall:",Recall12)
print("roc_auc_Score:",roc_auc_score(y_train1, pred_train5) )
print("f1_score:",f1_score(y_train1, pred_train5) )
print("accuracy_score:",accuracy_score(y_train1, pred_train5))

Precision: 1.0
Recall: 1.0
roc_auc_Score: 1.0
f1_score: 1.0
accuracy_score: 1.0

Test Scores¶

In [115]:
print(confusion_matrix(y_test1,pred_xgbc))

[[344  26]
 [ 42 328]]
In [116]:
g1 = confusion_matrix(y_test1,pred_xgbc)
TN = g1[0,0]
FP = g1[0,1]
FN = g1[1,0]
TP = g1[1,1]
precision13 = TP/float(TP+FP)
Recall13 = TP/float(TP+FN)
In [117]:
print("Precision:", precision13)
print("Recall:",Recall13)
print("roc_auc_Score:",roc_auc_score(y_test1,pred_xgbc) )
print("f1_score:",f1_score(y_test1,pred_xgbc) )
print("accuracy_score:",accuracy_score(y_test1,pred_xgbc))

Precision: 0.9265536723163842
Recall: 0.8864864864864865
roc_auc_Score: 0.9081081081081082
f1_score: 0.9060773480662982
accuracy_score: 0.9081081081081082
In [118]:
conf_mat = confusion_matrix(y_test1,pred_xgbc)
sns.heatmap(conf_mat, annot=True, fmt='d', cbar=False)
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Actual vs. Predicted Confusion Matrix')
b, t = plt.ylim() 
b += 0.5 
t -= 0.5 
plt.ylim(b, t) 
plt.show()

Cross Validation¶

In [119]:
from sklearn import model_selection
kf=model_selection.KFold(n_splits=5,shuffle=True,random_state=0)
values=model_selection.cross_val_score(xgbc,X_res, y_res,cv=kf,scoring='roc_auc')
print(values)
b_e=1-np.mean(values)
v_e=np.std(values,ddof=1)
print('Bias_error:',b_e)
print('VE:',v_e)
print("Accuracy: %0.2f (+/- %0.2f)" % (values.mean(), values.std() * 2))

[0.95228219 0.98554599 0.96784147 0.97096583 0.95622629]
Bias_error: 0.033427646727233196
VE: 0.013154483480689972
Accuracy: 0.97 (+/- 0.02)

Hyperparameter Tuning¶

In [120]:
from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
In [121]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
In [122]:
# Define the parameter grid for grid search
param_grid = {
    'n_estimators': [50, 100, 150],
    'max_depth': [5, 10, 15],
    'learning_rate': [0.01, 0.1, 0.2],
    'subsample': [0.5, 0.7, 1.0],
    'colsample_bytree': [0.5, 0.7, 1.0]
}
In [123]:
# Create the XGBClassifier object
xgb_classifier = XGBClassifier(use_label_encoder=False)
In [124]:
# Create the GridSearchCV object
grid_search = GridSearchCV(xgb_classifier, param_grid, cv=5, scoring='accuracy')
In [125]:
# Fit the data to perform grid search
grid_search.fit(X_train, y_train)
Out[125]:
GridSearchCV(cv=5,
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False, eval_metric=None,
                                     feature_types=None, gamma=None,
                                     gpu_id=None, grow_policy=None,
                                     importance_type=None,
                                     interaction_constraints=None,
                                     learning_rate=None,...
                                     max_delta_step=None, max_depth=None,
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     n_estimators=100, n_jobs=None,
                                     num_parallel_tree=None, predictor=None,
                                     random_state=None, ...),
             param_grid={'colsample_bytree': [0.5, 0.7, 1.0],
                         'learning_rate': [0.01, 0.1, 0.2],
                         'max_depth': [5, 10, 15],
                         'n_estimators': [50, 100, 150],
                         'subsample': [0.5, 0.7, 1.0]},
             scoring='accuracy')
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GridSearchCV(cv=5,
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False, eval_metric=None,
                                     feature_types=None, gamma=None,
                                     gpu_id=None, grow_policy=None,
                                     importance_type=None,
                                     interaction_constraints=None,
                                     learning_rate=None,...
                                     max_delta_step=None, max_depth=None,
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     n_estimators=100, n_jobs=None,
                                     num_parallel_tree=None, predictor=None,
                                     random_state=None, ...),
             param_grid={'colsample_bytree': [0.5, 0.7, 1.0],
                         'learning_rate': [0.01, 0.1, 0.2],
                         'max_depth': [5, 10, 15],
                         'n_estimators': [50, 100, 150],
                         'subsample': [0.5, 0.7, 1.0]},
             scoring='accuracy')
XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)
XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)
In [126]:
# Get the best parameters and best score from grid search
best_params = grid_search.best_params_
best_score = grid_search.best_score_
In [127]:
# Use the best parameters to create the final model
final_model = XGBClassifier(**best_params)
In [128]:
# Fit the final model on the training data
final_model.fit(X_train, y_train)
Out[128]:
XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=0.7, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=0.01, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=10, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=150, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)
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XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=0.7, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=0.01, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=10, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=150, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)

Train Score¶

In [129]:
# Make predictions on the train set
y_pred1 = final_model.predict(X_train)
In [130]:
# Calculate accuracy on the test set
accuracy = accuracy_score(y_train, y_pred1)
print("Accuracy:", accuracy)

Accuracy: 0.9217687074829932

Test Score¶

In [131]:
# Make predictions on the test set
y_pred = final_model.predict(X_test)
In [132]:
# Calculate accuracy on the test set
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

Accuracy: 0.8707482993197279

Hyper parameter Tuning¶

In [133]:
xgbc = XGBClassifier(use_label_encoder=False, eval_metric='logloss')
In [134]:
# Define the KFold cross-validation
kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=0)
In [135]:
# Create the GridSearchCV object
grid_search = GridSearchCV(estimator=xgbc, param_grid=param_grid, scoring='roc_auc', cv=kf, verbose=2)
In [136]:
# Fit GridSearchCV
grid_search.fit(X_res, y_res)

Fitting 5 folds for each of 243 candidates, totalling 1215 fits
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.3s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.5, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.5, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.5s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.3s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=0.7, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.4s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.01, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.1, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.3s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=0.7; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=50, subsample=1.0; total time=   0.0s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=5, n_estimators=150, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=10, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=50, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.5; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=0.7; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.1s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=100, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.5; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=0.7; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
[CV] END colsample_bytree=1.0, learning_rate=0.2, max_depth=15, n_estimators=150, subsample=1.0; total time=   0.2s
Out[136]:
GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False,
                                     eval_metric='logloss', feature_types=None,
                                     gamma=None, gpu_id=None, grow_policy=None,
                                     importance_type=None...
                                     max_delta_step=None, max_depth=None,
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     n_estimators=100, n_jobs=None,
                                     num_parallel_tree=None, predictor=None,
                                     random_state=None, ...),
             param_grid={'colsample_bytree': [0.5, 0.7, 1.0],
                         'learning_rate': [0.01, 0.1, 0.2],
                         'max_depth': [5, 10, 15],
                         'n_estimators': [50, 100, 150],
                         'subsample': [0.5, 0.7, 1.0]},
             scoring='roc_auc', verbose=2)
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GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False,
                                     eval_metric='logloss', feature_types=None,
                                     gamma=None, gpu_id=None, grow_policy=None,
                                     importance_type=None...
                                     max_delta_step=None, max_depth=None,
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     n_estimators=100, n_jobs=None,
                                     num_parallel_tree=None, predictor=None,
                                     random_state=None, ...),
             param_grid={'colsample_bytree': [0.5, 0.7, 1.0],
                         'learning_rate': [0.01, 0.1, 0.2],
                         'max_depth': [5, 10, 15],
                         'n_estimators': [50, 100, 150],
                         'subsample': [0.5, 0.7, 1.0]},
             scoring='roc_auc', verbose=2)
XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric='logloss',
              feature_types=None, gamma=None, gpu_id=None, grow_policy=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_bin=None, max_cat_threshold=None,
              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
              max_leaves=None, min_child_weight=None, missing=nan,
              monotone_constraints=None, n_estimators=100, n_jobs=None,
              num_parallel_tree=None, predictor=None, random_state=None, ...)
XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric='logloss',
              feature_types=None, gamma=None, gpu_id=None, grow_policy=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_bin=None, max_cat_threshold=None,
              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
              max_leaves=None, min_child_weight=None, missing=nan,
              monotone_constraints=None, n_estimators=100, n_jobs=None,
              num_parallel_tree=None, predictor=None, random_state=None, ...)
In [137]:
# Get the best score and best parameters
print("Best Score:", grid_search.best_score_)
print("Best Parameters:", grid_search.best_params_)

Best Score: 0.9741992232630985
Best Parameters: {'colsample_bytree': 1.0, 'learning_rate': 0.2, 'max_depth': 15, 'n_estimators': 100, 'subsample': 0.5}
In [138]:
# Perform cross-validation with the best parameters
values = model_selection.cross_val_score(XGBClassifier(**grid_search.best_params_), X_res, y_res, cv=kf, scoring='roc_auc')
In [139]:
# Print the individual cross-validation scores
print(values)

[0.96390302 0.99001749 0.97150447 0.97636447 0.96920666]
In [140]:
# Calculate and print the bias error and variance error
b_e = 1 - np.mean(values)
v_e = np.std(values, ddof=1)
print('Bias_error:', b_e)
print('VE:', v_e)

Bias_error: 0.02580077673690151
VE: 0.009913438821941059
In [141]:
# Print the average score and 95% confidence interval
print("Accuracy: %0.2f (+/- %0.2f)" % (values.mean(), values.std() * 2))

Accuracy: 0.97 (+/- 0.02)

Box-plots¶

In [142]:
results=[[0.79036426, 0.81985282, 0.77215127, 0.79231681, 0.77022926],
        [0.81265946, 0.88066363, 0.83406758, 0.85410494, 0.82248062],
        [0.96675061, 0.98966274, 0.97331947, 0.98065212, 0.97243114],
        [0.93047257, 0.97143847, 0.93449493, 0.95253144, 0.93496619],
        [0.92778134, 0.97894598, 0.96023496, 0.95488511, 0.93700313],
        [0.95228219, 0.98554599, 0.96784147, 0.97096583, 0.95622629]]
In [143]:
names=['Logistic_Regression','Decision Tree','Random Forest','Ada-boost','Bagging','XGBC']
In [144]:
fig = plt.figure(figsize=(12,8))
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
plt.grid(True)
ax.set_xticklabels(names)
plt.show()

From the box plot comparison diagram, we can see that Random Forest and XGBC are better models amongst all of the models. But when we observe train and test scores of Random Forest and XGBC, we can see that XGBC model is performing better on both train and test data. So we are finalizing best model as XGBC model.